This invention relates to the art of quantitative and qualitative mixture analysis, and more particularly to a new and improved apparatus for resolving spectral data of mixtures in terms of pure components and concentrations without the use of reference data in the form of known spectra or known concentrations.
In the analytical environment, despite the use of hyphenated and/or high resolution analytical instruments, the resulting spectral data often represents mixtures of several components. Furthermore, reference spectra are not always available to resolve the mixture data by techniques such as least squares or spectral subtraction. Accordingly, to extract information about pure components often is a major problem. For this type of problem, self-modeling mixture analysis techniques have been developed. Generally, the term curve resolution is used for the approaches of the type provided by the present invention. But since this technique is not limited to "curve" types of data, such as resulting from hyphenated instruments, the term self-modeling mixture analysis is used. Most prior art self-modeling techniques are based on principal component analysis. Principal component analysis is well suited for this type of problem because of the noise reduction that can be obtained by the use of the proper number of principal components and because of the ability to find pure variables, i.e. variables that have an intensity for only one of the components of the mixtures under consideration.
Although principal component analysis is presently the state-of-the-art approach for self-modeling curve resolution, it is far from a routine laboratory method. A notable exception is a commercially available apparatus which employs diode-array chromatographic detectors. This approach, however, is limited to resolving two components. There are several reasons for the limited success of principal component analysis as a routine tool. One is that despite the considerable amount of work done in the area of error analysis, there is no established method to determine the proper number of principal components to use. A second reason is that there is great reluctance to use principal component analysis, mainly due to the fact that principal component analysis does not lend itself easily to the development of user friendly programs that can be run with the same ease as, for example, library search programs for mass spectral matching. Thus, these techniques require highly skilled operators, due to the complexity of the algorithms used, and no general purpose software is available.
It would, therefore, be highly desirable to provide a new and improved apparatus for resolving spectral data of mixtures which is simple to use and does not require the use of reference data in the form of known spectra or known concentrations. It would be advantageous to provide such an apparatus which facilitates user interaction and which makes self-modeling mixture analysis more accessible for general use.